ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning.
Identifieur interne : 000051 ( Main/Exploration ); précédent : 000050; suivant : 000052ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning.
Auteurs : Jana Sperschneider [Australie] ; Peter N. Dodds [Australie] ; Karam B. Singh [Australie] ; Jennifer M. Taylor [Australie]Source :
- The New phytologist [ 1469-8137 ] ; 2018.
Descripteurs français
- KwdFr :
- Apprentissage machine (MeSH), Cystéine (métabolisme), Motifs d'acides aminés (MeSH), Oomycetes (métabolisme), Protéines fongiques (composition chimique), Protéines fongiques (métabolisme), Protéines végétales (composition chimique), Protéines végétales (métabolisme), Protéomique (MeSH), Signaux de triage des protéines (MeSH), Séquence conservée (MeSH), Séquence d'acides aminés (MeSH).
- MESH :
- composition chimique : Protéines fongiques, Protéines végétales.
- métabolisme : Cystéine, Oomycetes, Protéines fongiques, Protéines végétales.
- Apprentissage machine, Motifs d'acides aminés, Protéomique, Signaux de triage des protéines, Séquence conservée, Séquence d'acides aminés.
English descriptors
- KwdEn :
- Amino Acid Motifs (MeSH), Amino Acid Sequence (MeSH), Conserved Sequence (MeSH), Cysteine (metabolism), Fungal Proteins (chemistry), Fungal Proteins (metabolism), Machine Learning (MeSH), Oomycetes (metabolism), Plant Proteins (chemistry), Plant Proteins (metabolism), Protein Sorting Signals (MeSH), Proteomics (MeSH).
- MESH :
- chemical , chemistry : Fungal Proteins, Plant Proteins.
- chemical , metabolism : Cysteine, Fungal Proteins, Plant Proteins.
- metabolism : Oomycetes.
- Amino Acid Motifs, Amino Acid Sequence, Conserved Sequence, Machine Learning, Protein Sorting Signals, Proteomics.
Abstract
The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations. We present ApoplastP, the first method for predicting whether an effector or plant protein localizes to the apoplast. ApoplastP uncovers features of apoplastic localization common to both effectors and plant proteins, namely depletion in glutamic acid, acidic amino acids and charged amino acids and enrichment in small amino acids. ApoplastP predicts apoplastic localization in effectors with a sensitivity of 75% and a false positive rate of 5%, improving the accuracy of cysteine-rich classifiers by > 13%. ApoplastP does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted proteins. The secretomes of fungal saprophytes as well as necrotrophic, hemibiotrophic and extracellular fungal pathogens are enriched for predicted apoplastic proteins. Rust pathogens have low proportions of predicted apoplastic proteins, but these are highly enriched for predicted effectors. ApoplastP pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells.
DOI: 10.1111/nph.14946
PubMed: 29243824
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Taylor</name><affiliation wicri:level="1"><nlm:affiliation>Black Mountain Laboratories, CSIRO Agriculture and Food, Canberra, ACT, 2601, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Black Mountain Laboratories, CSIRO Agriculture and Food, Canberra, ACT, 2601</wicri:regionArea><wicri:noRegion>2601</wicri:noRegion></affiliation></author></analytic><series><title level="j">The New phytologist</title><idno type="eISSN">1469-8137</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Amino Acid Motifs (MeSH)</term><term>Amino Acid Sequence (MeSH)</term><term>Conserved Sequence (MeSH)</term><term>Cysteine (metabolism)</term><term>Fungal Proteins (chemistry)</term><term>Fungal Proteins (metabolism)</term><term>Machine Learning (MeSH)</term><term>Oomycetes (metabolism)</term><term>Plant Proteins (chemistry)</term><term>Plant Proteins (metabolism)</term><term>Protein Sorting Signals (MeSH)</term><term>Proteomics (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Apprentissage machine (MeSH)</term><term>Cystéine (métabolisme)</term><term>Motifs d'acides aminés (MeSH)</term><term>Oomycetes (métabolisme)</term><term>Protéines fongiques (composition chimique)</term><term>Protéines fongiques (métabolisme)</term><term>Protéines végétales (composition chimique)</term><term>Protéines végétales (métabolisme)</term><term>Protéomique (MeSH)</term><term>Signaux de triage des protéines (MeSH)</term><term>Séquence conservée (MeSH)</term><term>Séquence d'acides aminés (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Fungal Proteins</term><term>Plant Proteins</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Cysteine</term><term>Fungal Proteins</term><term>Plant Proteins</term></keywords><keywords scheme="MESH" qualifier="composition chimique" xml:lang="fr"><term>Protéines fongiques</term><term>Protéines végétales</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Oomycetes</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Cystéine</term><term>Oomycetes</term><term>Protéines fongiques</term><term>Protéines végétales</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Amino Acid Motifs</term><term>Amino Acid Sequence</term><term>Conserved Sequence</term><term>Machine Learning</term><term>Protein Sorting Signals</term><term>Proteomics</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Apprentissage machine</term><term>Motifs d'acides aminés</term><term>Protéomique</term><term>Signaux de triage des protéines</term><term>Séquence conservée</term><term>Séquence d'acides aminés</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations. We present ApoplastP, the first method for predicting whether an effector or plant protein localizes to the apoplast. ApoplastP uncovers features of apoplastic localization common to both effectors and plant proteins, namely depletion in glutamic acid, acidic amino acids and charged amino acids and enrichment in small amino acids. ApoplastP predicts apoplastic localization in effectors with a sensitivity of 75% and a false positive rate of 5%, improving the accuracy of cysteine-rich classifiers by > 13%. ApoplastP does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted proteins. The secretomes of fungal saprophytes as well as necrotrophic, hemibiotrophic and extracellular fungal pathogens are enriched for predicted apoplastic proteins. Rust pathogens have low proportions of predicted apoplastic proteins, but these are highly enriched for predicted effectors. ApoplastP pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29243824</PMID><DateCompleted><Year>2019</Year><Month>09</Month><Day>13</Day></DateCompleted><DateRevised><Year>2020</Year><Month>09</Month><Day>30</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1469-8137</ISSN><JournalIssue CitedMedium="Internet"><Volume>217</Volume><Issue>4</Issue><PubDate><Year>2018</Year><Month>03</Month></PubDate></JournalIssue><Title>The New phytologist</Title><ISOAbbreviation>New Phytol</ISOAbbreviation></Journal><ArticleTitle>ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning.</ArticleTitle><Pagination><MedlinePgn>1764-1778</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1111/nph.14946</ELocationID><Abstract><AbstractText>The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations. We present ApoplastP, the first method for predicting whether an effector or plant protein localizes to the apoplast. ApoplastP uncovers features of apoplastic localization common to both effectors and plant proteins, namely depletion in glutamic acid, acidic amino acids and charged amino acids and enrichment in small amino acids. ApoplastP predicts apoplastic localization in effectors with a sensitivity of 75% and a false positive rate of 5%, improving the accuracy of cysteine-rich classifiers by > 13%. ApoplastP does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted proteins. The secretomes of fungal saprophytes as well as necrotrophic, hemibiotrophic and extracellular fungal pathogens are enriched for predicted apoplastic proteins. Rust pathogens have low proportions of predicted apoplastic proteins, but these are highly enriched for predicted effectors. ApoplastP pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells.</AbstractText><CopyrightInformation>© 2017 CSIRO New Phytologist © 2017 New Phytologist Trust.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sperschneider</LastName><ForeName>Jana</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Centre for Environment and Life Sciences, CSIRO Agriculture and Food, Perth, WA, 6014, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Dodds</LastName><ForeName>Peter N</ForeName><Initials>PN</Initials><AffiliationInfo><Affiliation>Black Mountain Laboratories, CSIRO Agriculture and Food, Canberra, ACT, 2601, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Singh</LastName><ForeName>Karam B</ForeName><Initials>KB</Initials><AffiliationInfo><Affiliation>Centre for Environment and Life Sciences, CSIRO Agriculture and Food, Perth, WA, 6014, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Centre for Crop and Disease Management, Department of Environment and Agriculture, Curtin University, Bentley, WA, 6102, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Taylor</LastName><ForeName>Jennifer M</ForeName><Initials>JM</Initials><AffiliationInfo><Affiliation>Black Mountain Laboratories, CSIRO Agriculture and Food, Canberra, ACT, 2601, Australia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>12</Month><Day>15</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>New Phytol</MedlineTA><NlmUniqueID>9882884</NlmUniqueID><ISSNLinking>0028-646X</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D005656">Fungal Proteins</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D010940">Plant Proteins</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D021382">Protein Sorting Signals</NameOfSubstance></Chemical><Chemical><RegistryNumber>K848JZ4886</RegistryNumber><NameOfSubstance UI="D003545">Cysteine</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D020816" MajorTopicYN="N">Amino Acid Motifs</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000595" MajorTopicYN="N">Amino Acid Sequence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017124" MajorTopicYN="N">Conserved Sequence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003545" MajorTopicYN="N">Cysteine</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005656" MajorTopicYN="N">Fungal Proteins</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="Y">Machine Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009868" MajorTopicYN="N">Oomycetes</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010940" MajorTopicYN="N">Plant Proteins</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D021382" MajorTopicYN="N">Protein Sorting Signals</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D040901" MajorTopicYN="N">Proteomics</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">ApoplastP
</Keyword><Keyword MajorTopicYN="Y">apoplast</Keyword><Keyword MajorTopicYN="Y">apoplastic localization</Keyword><Keyword MajorTopicYN="Y">effectors</Keyword><Keyword MajorTopicYN="Y">machine learning</Keyword><Keyword MajorTopicYN="Y">plant pathogens</Keyword><Keyword MajorTopicYN="Y">plant proteomics</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2017</Year><Month>07</Month><Day>31</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2017</Year><Month>11</Month><Day>07</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2017</Year><Month>12</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>9</Month><Day>14</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2017</Year><Month>12</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">29243824</ArticleId><ArticleId IdType="doi">10.1111/nph.14946</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Australie</li></country></list><tree><country name="Australie"><noRegion><name sortKey="Sperschneider, Jana" sort="Sperschneider, Jana" uniqKey="Sperschneider J" first="Jana" last="Sperschneider">Jana Sperschneider</name></noRegion><name sortKey="Dodds, Peter N" sort="Dodds, Peter N" uniqKey="Dodds P" first="Peter N" last="Dodds">Peter N. Dodds</name><name sortKey="Singh, Karam B" sort="Singh, Karam B" uniqKey="Singh K" first="Karam B" last="Singh">Karam B. Singh</name><name sortKey="Singh, Karam B" sort="Singh, Karam B" uniqKey="Singh K" first="Karam B" last="Singh">Karam B. Singh</name><name sortKey="Taylor, Jennifer M" sort="Taylor, Jennifer M" uniqKey="Taylor J" first="Jennifer M" last="Taylor">Jennifer M. Taylor</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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